"""
================================
Select electrodes and resampling
================================

Within paradigm, it is possible to restrict analysis only to a subset of
electrodes and to resample to a specific sampling rate. There is also a
utility function to select common electrodes shared between datasets.
This tutorial demonstrates how to use this functionality.
"""
# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
#
# License: BSD (3-clause)
from moabb.datasets import BNCI2014001, Zhou2016
from moabb.paradigms import LeftRightImagery
from moabb.evaluations import WithinSessionEvaluation
from moabb.datasets.utils import find_intersecting_channels

from sklearn.pipeline import make_pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.linear_model import LogisticRegression as LR

from mne.decoding import CSP
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace

import matplotlib.pyplot as plt
import moabb.analysis.plotting as moabb_plt

##############################################################################
# Datasets
# --------
#
# Select datasets for motor imagery

datasets = [Zhou2016(), BNCI2014001()]

##############################################################################
# Paradigm
# --------
#
# Restrict further analysis to specified channels, here C3, C4, and Cz.
# Also, use a specific resampling. In this example, all datasets are
# set to 200 Hz.

paradigm = LeftRightImagery(channels=['C3', 'C4', 'Cz'], resample=200.)

##############################################################################
# Evaluation
# ----------
#
# The evaluation is conducted on with CSP+LDA, only on the 3 electrodes, with
# a sampling rate of 200 Hz.

evaluation = WithinSessionEvaluation(paradigm=paradigm,
                                     datasets=datasets)
csp_lda = make_pipeline(CSP(n_components=2), LDA())
ts_lr = make_pipeline(Covariances(estimator='oas'),
                      TangentSpace(metric='riemann'),
                      LR(C=1.0))
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())

##############################################################################
# Electrode selection
# -------------------
#
# It is possible to select the electrodes that are shared by all datasets
# using the `find_intersecting_channels` function. Datasets that have 0
# overlap with others are discarded. It returns the set of common channels,
# as well as the list of datasets with valid channels.

electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(paradigm=paradigm,
                                     datasets=datasets,
                                     overwrite=True)
results = evaluation.process({'csp+lda': csp_lda, 'ts+lr': ts_lr})
print(results.head())

##############################################################################
# Plot results
# ------------
#
# Compare the obtained results with the two pipelines, CSP+LDA and logistic
# regression computed in the tangent space of the covariance matrices.

fig = moabb_plt.paired_plot(results, 'csp+lda', 'ts+lr')
plt.show()
